tumor heterogeneity in breast cancer, concepts and tools

Tumor Heterogeneity in Breast Cancer, Concepts and Tools

Anthony M Magliocco MD FRCPC FCAP Chair of Anatomical Pathology and

Executive Director of Esoteric Laboratory Services H. Lee Moffitt Cancer Center

March 13, 2013

Disclosures

§  Ventana Medical Systems

Overview

§  Discussion of Tumor Heterogeneity §  Tools for analysis

w  IHC w  Image analysis w  Genomics w  CTCs

§  Future directions

http://library.med.utah.edu/WebPath/jpeg3/BREST003.jpg

Geographic and Temporal Variation in Tumors

Estrogen Receptor

HER2

Basal Keratins

EGFR

Molecular Breast Classification

Tumor Evolution

Invasion

Morphology of Tumor Progression

Lympahtic Space

Invasive Ductal Cacinoma

http://www.flagshipbio.com/wp-content/uploads/2011/09/Heterogeneity-of-HER2-staining-in-breast-cancer.png

HER2 Heterogeneity

http://www.sciencedirect.com/science/article/pii/S0304419X09000742#gr1

TUMOR EVOLUTION

http://www.nature.com/nrg/journal/v13/n11/images/nrg3317-f2.jpg

§ Immunohistochemistry is a powerful analytical tool

Estrogen receptor

Estrogen Receptor

Copyright © American Society of Clinical Oncology

Harvey, J. M. et al. J Clin Oncol; 17:1474 1999

Fig 2. Univariate DFS curves for all possible total IHC scores in patients receiving any adjuvant endocrine therapy (almost always tamoxifen)

ACIS EVALUATION of Ki67

MAGLIOCCO LABORATORIES SABCS 2010

Finding the Cut Point

MAGLIOCCO LABORATORIES SABCS 2010

16.25%

MAGLIOCCO LABORATORIES SABCS 2010

16.25%

MAGLIOCCO LABORATORIES SABCS 2010

FLUORESCENT IMAGING

•  Several Advantages over DAB – More markers – Greater Dynamic Range – Easier channel separation for imaging

PR DAPI CTK

Composite Magliocco Laboratory

DAB-­‐IHC  vs  IF-­‐IHC  

AQUA  =  Average  target  pixel  intensity/Area  of  the  defined  compartment  

DAB  /  Pathologist   HistoRx  AQUA  

AQUA Scoring is Reproducible

R² = 0.99502

0

2000

4000

6000

8000

10000

12000

0 2000 4000 6000 8000 10000 12000

Her

2 C

ytop

lasm

ic A

QU

A - 0

3Dec

10

Her2 Cytoplasmic AQUA - 30Nov10

Run to Run Variation: Her2 cAQUA (Serial Sections)

Spearman = 0.986

ER AQUA interlab

HistoRx  Scoring  vs  Pathologist  Scoring  •     ER  scoring  from  serial  secCons  of  an  18  TMA  Breast  Cancer  series  

•     Stained  by  DAB-­‐IHC  and  scored  by  a  pathologist  or  Stained  by  IF-­‐IHC  and    scored  by  AQUA  

Pathologist  Score  

AQUA  Score  

ER:  Pathologist  vs  AQUA    (All  PaCents)  

                   DAB-­‐IHC                                          vs                                        AQUA-­‐IHC  

Pathologist Score

X-tile analysis

AQUATM: ERCC1 Cervix

Magliocco

Created by Tex

0 2 4 6 8 10

Year of Study

0.0

0.2

0.4

0.6

0.8

1.0

Adj

uste

d Pr

opor

tion

Aliv

e (O

vera

ll)

p-value = 0.52

IHC = 0,1,2

IHC = 3

Overall survival by ERCC1, IHC score

Created by Tex

Overall survival by ERCC1, AQUA™ score

0 2 4 6 8 10

Year of Study

0.0

0.2

0.4

0.6

0.8

1.0

Adj

uste

d Pr

opor

tion

Aliv

e (O

vera

ll)

p-value = 0.031

AQUA < 975

AQUA > 975

ERCC1 8F1 ERCC1 FL297

ERCC1 8F1 vs FL297

AQUA  measurment  of  ERCC1  expression  in  Nucleus  of  TMA  specimens  

Ki67

pS6

pS6 different antibody clones via AQUA analysis – Cervix Cancer

pS6 AQUA®

cytoplasmic

pS6 status and OS

High pS6 status was associated with better overall survival in the RT+chemo cohort

High

Low

EGFR Ki67 CTK DAPI

Masking EGFR High vs EGFR Low Tumour Areas

Low  EGFR/Ki67  RaCo  Predicts  Poor  Survival  

Kaplan-­‐Meier  survival  curves  measuring  the  overall  disease  specific  survival  based  on  straCficaCon  by  EGFR/Ki67  RaCo.    The  average  raCo  for  each  paCent  (from  replicate  histospots)  was  used  and  paCents  were  categorized  as  having  a  high  raCo  if  they  fell  within  the  top  3  quarCles  of  expressers  (n=68).  Five  year  esCmates   for  overall   survival  are  78%  for  high  RaCo  paCents  and  39%  for   low  RaCo  paCents.    

RTOG  0128  cd34  vessel  density  

Core  20  1%  

core  84:        459.4                                                      core  50:      59.9   "

Cervical  Carcinoma  –  CAIX  (AQUA™)  CAIX

Stromal Caix in HPV neg HN cancer

Developing a scoring method to examine the relationship between CAIX and Ki67

CAIX and Ki-67 CARO CERVIX COHORT OS

High Ki-67 within CAIX high tumor regions was associated with better OS [HR 0.83 (0.7-0.97), p=0.023]

Tonsil CD4 red CD8 blue

PCK=Green      CD4=Red      CD8=Blue  

Two  tumors  with  strong  tumor  and  stroma    CD4/CD8  cell  staining  

CD4/CD8  RaCo  May  Predict  Treatment  Response  

CD4 RED

CD8 BLUE

FRACTAL GEOMETRY IN CANCER

Prostate  Cancer:    Gleason  Grading  System  

Low Grade (Well

Differentiate) •  Slow growing •  Look similar to

normal cells •  Less

aggressive

Schematic Diagram by Dr. D. F. Gleason http://www.cancer.prostate-help.org/cagleas.htm

High Grade (Poorly

Differentiated) •  Fast growing •  Look very

different from normal cells

•  Very aggressive (spread quickly)

Fractal Geometry

Fractal Dimension •  Can be used as a measure of the level of

structural complexity

FD = 1

FD = 1

FD = 1.5

Methods: Staining Example: Breast Cancer Specimens

Hematoxylin & Eosin Pan-keratin

Methods: Staining and Segmented Structures

75.1=BD

69.1=BD

H&E

Pan-keratin MAGLIOCCO LABORATORIES

Results: Effects of Staining (Prostate Cancer)

Tambasco, M. Magliocco. .Micron.2008

1.2

1.28

1.36

1.44

1.52

1.6

1.68

1.76

1.84

1.92

2

Benign HighGrade

Benign HighGrade

Frac

tal D

imen

sion

H&E Pan-Keratin

Sample Sizes 63 Benign 19 High Grade

Calgary Tamoxifen Cohort

•  Retrospective annotated series Cases between 1980-1999

•  Over 800 Cases enriched with 200 events •  No chemotherapy •  Tamoxifen given to many regardless of ER

status

Kaplan-Meier Estimate of Survival

FD < 1.71

FD ≥ 1.71

369 Breast Cancer Patients Optimal fractal dimension cut-point = 1.71

p = 0.002

MAGLIOCCO LABORATORIES

MAGLIOCCO LABORATORIES SABCS 2010

Validation: Fractal Map

1.8

1.8 1.8

1.8

1.5

1.5

1.5

1.5

1.4

1.4 1.4

1.4

1.7

1.7

1.7

1.7

1.2

1.2 1.2

1.2

1.3

1.3

1.3

1.6

1.6

Outline of Takagi Surface Fractal Map

MAGLIOCCO LABORATORIES

Local Fractal Dimension

Fractal Dimension Map

MAGLIOCCO LABORATORIES

Fractal Map: Breast Cancer

1

3

2

4

1

3

2

4

MAGLIOCCO LABORATORIES

OTHER SOURCES OF HETERGENEITY

Quebec probes flawed cancer tests Health officials compare faulty breast exam results to problems in Newfoundland, promise fast action

PROBLEMS WITH “ROUTINE” TESTING

National Standards Immuno cIQc

CA

SE

Laboratory

Pre-Analytic

Analytical

Post-Analytical

Understanding  VariaCon  in  Biomarker  Analysis  

Specimen  quality  PreparaCons  

Immunohistochemistry  Use  automated  system  

standardize  scoring  

http://www.nasa.gov/images/content/235791main_image_1098_946-710.jpg

http://news.nationalgeographic.co.uk/news/2009/06/photogalleries/fathers-day-2009-animal-dads-pictures/images/primary/090618-07-greatest-animal-dads-emperor-penguin_big.jpg

.

Rakha E A et al. JCO 2008;26:3153-3158

©2008 by American Society of Clinical Oncology

Grade and Outcome Breast Ca

Before 1989

After 1989

Grade Changes with Treatment

Next Generation Sequencing

Emerging Breast Cancer Biomarkers?

DC Koboldt et al. Nature 000, 1-10 (2012) doi:10.1038/nature11412

Intratumoral genetic heterogeneity in an advanced primary CRC. Three-dimensional reconstruction of a tumor (Table IV, case 4) that was divided into two parts (1 and 2) and then

serially sectioned into five slices (A–E) of ∼4.5 mm thickness.

Losi L et al. Carcinogenesis 2005;26:916-922

Carcinogenesis vol.26 no.5 © Oxford University Press 2005; all rights reserved.

Copyright ©2004 American Association for Cancer Research

Allard, W. J. et al. Clin Cancer Res 2004;10:6897-6904

Tumor Cells Circulate in the Peripheral Blood of All Major Carcinomas but not in Healthy Subjects or Patients With Nonmalignant Diseases W. Jeffrey Allard1, Jeri Matera1, M. Craig Miller1, Madeline Repollet1, Mark C. Connelly1, Chandra Rao1, Arjan G. J. Tibbe1, Jonathan W. Uhr2 and Leon W. M. M. Terstappen1

Circulating tumor cells are found in patients with metastasis, and predict survival in breast cancer

Summary

§  Tumors evolve in 4d §  We need better assays and tools for

reproducible measurements §  We need greater focus on reducing pre

analytical variation §  We need better collections of tumours after

treatment and in metastatic setting

Future

§  Anatomical Pathology

w  Integration of: ● Medical data ● Imaging ● Advanced Histology ● Genomics ● CTCs and biomarkers ● Huge amounts of multidimensional data requiring

systems biology aproaches

MAGLIOCCO LABORATORIES Tom Baker Cancer Centre 2010

Acknowledgements  -­‐  Funding